PCGR - Personal Cancer Genome Reporter

Thank you for asking about PCGR - Personal Cancer Genome Reporter'. It is a software tool designed to help clinicians interpret individual tumor genomes by analyzing somatic variants and presenting the results in a format accessible to clinical experts. The tool uses a comprehensive set of knowledge resources related to tumor biology and therapeutic biomarkers, both at the gene and variant level. It generates a tiered report that highlights the most important findings and provides recommendations for further analysis or treatment options. PCGR is implemented in Python/R and is freely available through Docker technology. You can find documentation, example reports, and installation instructions on its GitHub page. If you have any questions or need assistance with using PCGR, please feel free to contact me.

Topic

Oncology;Genomics;Biotherapeutics;Genetic variation;DNA structural variation;Personalised medicine;Data visualisation;Functional genomics

Detail

  • Operation: Gene functional annotation;Variant prioritisation;Variant classification;Variant effect prediction

  • Software interface: Command-line interface

  • Language: R,Python

  • License: The MIT License

  • Cost: Free with restrictions

  • Version name: 1.0.2

  • Credit: The Division of Intramural Research of NHLBI, NIH and the 4DN Transformative Collaborative Project Award, National Key Research and Development Project.

  • Input: Sequence variations [VCF], Text data [TSV]

  • Output: Sequence variations [VCF], Report [HTML], Report [JSON], Report [TSV]

  • Contact: Sigve Nakken sigven@ifi.uio.no ,Peter Diakumis peter.diakumis@umccr.org

  • Collection: -

  • Maturity: Mature

Publications

  • Personal Cancer Genome Reporter: variant interpretation report for precision oncology.
  • Nakken S, et al. Personal Cancer Genome Reporter: variant interpretation report for precision oncology. Personal Cancer Genome Reporter: variant interpretation report for precision oncology. 2018; 34:1778-1780. doi: 10.1093/bioinformatics/btx817
  • https://doi.org/10.1093/bioinformatics/btx817
  • PMID: 29272339
  • PMC: PMC5946881

Download and documentation

< Back to DB search